Semantic caching is a technique that reuses an earlier AI response when a new request is close enough in meaning to a previous one. Instead of running full inference for every prompt, the system compares the new input with cached queries and returns a stored answer when the match is similar enough. This reduces latency, cloud spend, and compute load, especially in chatbots, search assistants, and retrieval pipelines.
In cyber security, semantic caching matters because it can lower the cost of defensive AI tools such as SOC copilots, triage assistants, and internal knowledge bots. It also introduces risk: if cache boundaries are weak, one tenant may receive another tenant’s content, sensitive prompts may be exposed, or a poisoned cached answer may be replayed to many users. Attackers may try to game similarity thresholds with prompt variations, while defenders limit this by using strict access control, cache expiration, input normalization, and monitoring for unusual reuse patterns.